Effect of Spirulina Microalgae Powder in Gluten-Free Biscuits and Snacks Formulated with Quinoa Flour
Bibliographic record
Abstract
This study evaluated the effects of incorporating spirulina algae powder (SAP) at 3%, 6%, and 9% into quinoa flour (QF) blends to produce gluten-free biscuits and snacks, compared to a 100% QF control. The chemical composition, mineral and amino acid content, antioxidant capacity, starch gelatinization, color, baking quality, sensory properties, and texture were analyzed. SAP was found to have high protein (62.50%), fat (5.92%), and ash (12.90%) content. Increasing the SAP concentration in QF blends resulted in a dose-dependent enhancement in the nutritional value of the biscuits and snacks. Farinograph analysis indicated a positive relationship between SAP percentage and water absorption. The inclusion of SAP significantly altered differential scanning calorimetry (DSC) and viscoamylograph parameters. Biscuit weight, volume, and specific volume decreased with increasing SAP levels. Hunter color measurements showed a SAP concentration-dependent darkening effect, which was supported by sensory assessments. The 9% SAP biscuits and snacks exhibited the greatest antioxidant activity, with DPPH values of 50.18 and 43.6 µmol/g, respectively, and reducing power values of 41.49 and 36.58 µmol/g, respectively. Overall, while all samples were deemed acceptable, the 3% and 6% SAP formulations generally demonstrated better sensory characteristics and improved nutritional profiles, suggesting their potential as suitable options for individuals with gluten sensitivities.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".